Noninvasive Multimodal Imaging to Predict Recovery of Locomotion after Extended Limb Ischemia

Acute limb ischemia is a common cause of morbidity and mortality following trauma both in civilian centers and in combat related injuries. Rapid determination of tissue viability and surgical restoration of blood flow are desirable, but not always possible. We sought to characterize the response to increasing periods of hind limb ischemia in a porcine model such that we could define a period of critical ischemia (the point after which irreversible neuromuscular injury occurs), evaluate non-invasive methods for characterizing that ischemia, and establish a model by which we could predict whether or not the animal’s locomotion would return to baselines levels post-operatively. Ischemia was induced by either application of a pneumatic tourniquet or vessel occlusion (performed by clamping the proximal iliac artery and vein at the level of the inguinal ligament). The limb was monitored for the duration of the procedure with both 3-charge coupled device (3CCD) and infrared (IR) imaging for tissue oxygenation and perfusion, respectively. The experimental arms of this model are effective at inducing histologically evident muscle injury with some evidence of expected secondary organ damage, particularly in animals with longer ischemia times. Noninvasive imaging data shows excellent correlation with post-operative functional outcomes, validating its use as a non-invasive means of viability assessment, and directly monitors post-occlusive reactive hyperemia. A classification model, based on partial-least squares discriminant analysis (PLSDA) of imaging variables only, successfully classified animals as “returned to normal locomotion” or “did not return to normal locomotion” with 87.5% sensitivity and 66.7% specificity after cross-validation. PLSDA models generated from non-imaging data were not as accurate (AUC of 0.53) compared the PLSDA model generated from only imaging data (AUC of 0.76). With some modification, this limb ischemia model could also serve as a means on which to test therapies designed to prolong the time before critical ischemia.     

Statistical data processing in clinical proteomics

This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature.     

Automatic Assignment of EC Numbers

A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system, e.g., drug design, metabolic network reconstruction and system biology. When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function, the EC number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB) is used. However, each and every one of these applications is critically dependent upon the consistency and reliability of the underlying data for success. We have developed tools for the validation of the EC number classification scheme. In this paper, we present validated data of 3788 enzymatic reactions including 229 sub-subclasses of the EC classification system. Over 80% agreement was found between our assignment and the EC classification. For 61 (i.e., only 2.5%) reactions we found that their assignment was inconsistent with the rules of the nomenclature committee; they have to be transferred to other sub-subclasses. We demonstrate that our validation results can be used to initiate corrections and improvements to the EC number classification scheme.     

A blocking strategy to improve gene selection for classification of gene expression data

Because of high dimensionality, machine learning algorithms typically rely on feature selection techniques in order to perform effective classification in microarray gene expression data sets. However, the large number of features compared to the number of samples makes the task of feature selection computationally hard and prone to errors. This paper interprets feature selection as a task of stochastic optimization, where the goal is to select among an exponential number of alternative gene subsets the one expected to return the highest generalization in classification. Blocking is an experimental design strategy which produces similar experimental conditions to compare alternative stochastic configurations in order to be confident that observed differences in accuracy are due to actual differences rather than to fluctuations and noise effects. We propose an original blocking strategy for improving feature selection which aggregates in a paired way the validation outcomes of several learning algorithms to assess a gene subset and compare it to others. This is a novelty with respect to conventional wrappers, which commonly adopt a sole learning algorithm to evaluate the relevance of a given set of variables. The rationale of the approach is that, by increasing the amount of experimental conditions under which we validate a feature subset, we can lessen the problems related to the scarcity of samples and consequently come up with a better selection. The paper shows that the blocking strategy significantly improves the performance of a conventional forward selection for a set of 16 publicly available cancer expression data sets. The experiments involve six different classifiers and show that improvements take place independent of the classification algorithm used after the selection step. Two further validations based on available biological annotation support the claim that blocking strategies in feature selection may improve the accuracy and the quality of the solution. The first validation is based on retrieving PubMEd abstracts associated to the selected genes and matching them to regular expressions describing the biological phenomenon underlying the expression data sets. The biological validation that follows is based on the use of the Bioconductor package GoStats in order to perform Gene Ontology statistical analysis.     

Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool

An MS-based metabolomics strategy including variable selection and PLSDA analysis has been assessed as a tool to discriminate between non-steatotic and steatotic human liver profiles. Different chemometric approaches for uninformative variable elimination were performed by using two of the most common software packages employed in the field of metabolomics (i.e., MATLAB and SIMCA-P). The first considered approach was performed with MATLAB where the PLS regression vector coefficient values were used to classify variables as informative or not. The second approach was run under SIMCA-P, where variable selection was performed according to both the PLS regression vector coefficients and VIP scores. PLSDA models performance features, such as model validation, variable selection criteria, and potential biomarker output, were assessed for comparison purposes. One interesting finding is that variable selection improved the classification predictiveness of all the models by facilitating metabolite identification and providing enhanced insight into the metabolic information acquired by the UPLC-MS method. The results prove that the proposed strategy is a potentially straightforward approach to improve model performance. Among others, GSH, lysophospholipids and bile acids were found to be the most important altered metabolites in the metabolomic profiles studied. However, further research and more in-depth biochemical interpretations are needed to unambiguously propose them as disease biomarkers.     

Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

In this paper we introduce discriminant Q² (DQ²) as an improvement for the Q² value used in the validation of PLSDA models. DQ² does not penalize class predictions beyond the class label value. With rigorous Monte Carlo simulations we show that when DQ² is used, a smaller effect can be found statistically significant than when the standard Q² is used.     

Feature selection in validating mass spectrometry database search results

Tandem mass spectrometry (MS/MS) combined with protein database searching has been widely used in protein identification. A validation procedure is generally required to reduce the number of false positives. Advanced tools using statistical and machine learning approaches may provide faster and more accurate validation than manual inspection and empirical filtering criteria. In this study, we use two feature selection algorithms based on random forest and support vector machine to identify peptide properties that can be used to improve validation models. We demonstrate that an improved model based on an optimized set of features reduces the number of false positives by 58% relative to the model which used only search engine scores, at the same sensitivity score of 0.8. In addition, we develop classification models based on the physicochemical properties and protein sequence environment of these peptides without using search engine scores. The performance of the best model based on the support vector machine algorithm is at 0.8 AUC, 0.78 accuracy, and 0.7 specificity, suggesting a reasonably accurate classification. The identified properties important to fragmentation and ionization can be either used in independent validation tools or incorporated into peptide sequencing and database search algorithms to improve existing software programs.     

A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics

In this paper, we compare the performance of six different feature selection methods for LC-MS-based proteomics and metabolomics biomarker discovery—t test, the Mann–Whitney–Wilcoxon test (mww test), nearest shrunken centroid (NSC), linear support vector machine–recursive features elimination (SVM-RFE), principal component discriminant analysis (PCDA), and partial least squares discriminant analysis (PLSDA)—using human urine and porcine cerebrospinal fluid samples that were spiked with a range of peptides at different concentration levels. The ideal feature selection method should select the complete list of discriminating features that are related to the spiked peptides without selecting unrelated features. Whereas many studies have to rely on classification error to judge the reliability of the selected biomarker candidates, we assessed the accuracy of selection directly from the list of spiked peptides. The feature selection methods were applied to data sets with different sample sizes and extents of sample class separation determined by the concentration level of spiked compounds. For each feature selection method and data set, the performance for selecting a set of features related to spiked compounds was assessed using the harmonic mean of the recall and the precision (f-score) and the geometric mean of the recall and the true negative rate (g-score). We conclude that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes (n = 6), but their performance improves markedly with increasing sample size up to a point (n > 12) at which they outperform the other methods. PCDA and PLSDA select small feature sets with high precision but miss many true positive features related to the spiked peptides. NSC strikes a reasonable compromise between recall and precision for all data sets independent of spiking level and number of samples. Linear SVM-RFE performs poorly for selecting features related to the spiked compounds, even though the classification error is relatively low.Biomarkers play an important role in advancing medical research through the early diagnosis of disease and prognosis of treatment interventions (1, 2). Biomarkers may be proteins, peptides, or metabolites, as well as mRNAs or other kinds of nucleic acids (e.g. microRNAs) whose levels change in relation to the stage of a given disease and which may be used to accurately assign the disease stage of a patient. The accurate selection of biomarker candidates is crucial, because it determines the outcome of further validation studies and the ultimate success of efforts to develop diagnostic and prognostic assays with high specificity and sensitivity. The success of biomarker discovery depends on several factors: consistent and reproducible phenotyping of the individuals from whom biological samples are obtained; the quality of the analytical methodology, which in turn determines the quality of the collected data; the accuracy of the computational methods used to extract quantitative and molecular identity information to define the biomarker candidates from raw analytical data; and finally the performance of the applied statistical methods in the selection of a limited list of compounds with the potential to discriminate between predefined classes of samples. De novo biomarker research consists of a biomarker discovery part and a biomarker validation part (3). Biomarker discovery uses analytical techniques that try to measure as many compounds as possible in a relatively low number of samples. The goal of subsequent data preprocessing and statistical analysis is to select a limited number of candidates, which are subsequently subjected to targeted analyses in large number of samples for validation.Advanced technology, such as high-performance liquid chromatography–mass spectrometry (LC-MS),¹ is increasingly applied in biomarker discovery research. Such analyses detect tens of thousands of compounds, as well as background-related signals, in a single biological sample, generating enormous amounts of multivariate data. Data preprocessing workflows reduce data complexity considerably by trying to extract only the information related to compounds resulting in a quantitative feature matrix, in which rows and columns correspond to samples and extracted features, respectively, or vice versa. Features may also be related to data preprocessing artifacts, and the ratio of such erroneous features to compound-related features depends on the performance of the data preprocessing workflow (4). Preprocessed LC-MS data sets contain a large number of features relative to the sample size. These features are characterized by their m/z value and retention time, and in the ideal case they can be combined and linked to compound identities such as metabolites, peptides, and proteins. In LC-MS-based proteomics and metabolomics studies, sample analysis is so time consuming that it is practically impossible to increase the number of samples to a level that balances the number of features in a data set. Therefore, the success of biomarker discovery depends on powerful feature selection methods that can deal with a low sample size and a high number of features. Because of the unfavorable statistical situation and the risk of overfitting the data, it is ultimately pivotal to validate the selected biomarker candidates in a larger set of independent samples, preferably in a double-blinded fashion, using targeted analytical methods (1).Biomarker selection is often based on classification methods that are preceded by feature selection methods (filters) or which have built-in feature selection modules (wrappers and embedded methods) that can be used to select a list of compounds/peaks/features that provide the best classification performance for predefined sample groups (e.g. healthy versus diseased) (5). Classification methods are able to classify an unknown sample into a predefined sample class. Univariate feature selection methods such as filters (t test or Wilcoxon–Mann–Whitney tests) cannot be used for sample classification. Other classification methods such as the nearest shrunken centroid method have intrinsic feature selection ability, whereas other classification methods such as principal component discriminant analysis (PCDA) and partial least squares regression coupled with discriminant analysis (PLSDA) should be augmented with a feature selection method. There are classifiers having no feature selection option that perform the classification using all variables, such as support vector machines that use non-linear kernels (6). Classification methods without the ability to select features cannot be used for biomarker discovery, because these methods aim to classify samples into predefined classes but cannot identify the limited number of variables (features or compounds) that form the basis of the classification (6, 7). Different statistical methods with feature selection have been developed according to the complexity of the analyzed data, and these have been extensively reviewed (5, 6, 8, 9). Ways of optimizing such methods to improve sensitivity and specificity are a major topic in current biomarker discovery research and in the many “omics-related” research areas (6, 10, 11). Comparisons of classification methods with respect to their classification and learning performance have been initiated. Van der Walt et al. (12) focused on finding the most accurate classifiers for simulated data sets with sample sizes ranging from 20 to 100. Rubingh et al. (13) compared the influence of sample size in an LC-MS metabolomics data set on the performance of three different statistical validation tools: cross validation, jack-knifing model parameters, and a permutation test. That study concluded that for small sample sets, the outcome of these validation methods is influenced strongly by individual samples and therefore cannot be trusted, and the validation tool cannot be used to indicate problems due to sample size or the representativeness of sampling. This implies that reducing the dimensionality of the feature space is critical when approaching a classification problem in which the number of features exceeds the number of samples by a large margin. Dimensionality reduction retains a smaller set of features to bring the feature space in line with the sample size and thus allow the application of classification methods that perform with acceptable accuracy only when the sample size and the feature size are similar.In this study we compared different classification methods focusing on feature selection in two types of spiked LC-MS data sets that mimic the situation of a biomarker discovery study. Our results provide guidelines for researchers who will engage in biomarker discovery or other differential profiling “omics” studies with respect to sample size and selecting the most appropriate feature selection method for a given data set. We evaluated the following approaches: univariate t test and Mann–Whitney–Wilcoxon test (mww test) with multiple testing correction (14), nearest shrunken centroid (NSC) (15, 16), support vector machine–recursive features elimination (SVM-RFE) (17), PLSDA (18), and PCDA (19). PCDA and PLSDA were combined with the rank-product as a feature selection criterion (20). These methods were evaluated with data sets having three characteristics: different biological background, varying sample size, and varying within- and between-class variability of the added compounds. Data were acquired via LC-MS from human urine and porcine cerebrospinal fluid (CSF) samples that were spiked with a set of known peptides (true positives) at different concentration levels. These samples were then combined in two classes containing peptides spiked at low and high concentration levels. The performance of the classification methods with feature selection was measured based on their ability to select features that were related to the spiked peptides. Because true positives were known in our data set, we compared performance based on the f-score (the harmonic mean of precision and recall) and the g-score (the geometric mean of accuracy).     

Scientific knowledge is possible with small-sample classification

A typical small-sample biomarker classification paper discriminates between types of pathology based on, say, 30,000 genes and a small labeled sample of less than 100 points. Some classification rule is used to design the classifier from this data, but we are given no good reason or conditions under which this algorithm should perform well. An error estimation rule is used to estimate the classification error on the population using the same data, but once again we are given no good reason or conditions under which this error estimator should produce a good estimate, and thus we do not know how well the classifier should be expected to perform. In fact, virtually, in all such papers the error estimate is expected to be highly inaccurate. In short, we are given no justification for any claims.Given the ubiquity of vacuous small-sample classification papers in the literature, one could easily conclude that scientific knowledge is impossible in small-sample settings. It is not that thousands of papers overtly claim that scientific knowledge is impossible in regard to their content; rather, it is that they utilize methods that preclude scientific knowledge. In this paper, we argue to the contrary that scientific knowledge in small-sample classification is possible provided there is sufficient prior knowledge. A natural way to proceed, discussed herein, is via a paradigm for pattern recognition in which we incorporate prior knowledge in the whole classification procedure (classifier design and error estimation), optimize each step of the procedure given available information, and obtain theoretical measures of performance for both classifiers and error estimators, the latter being the critical epistemological issue. In sum, we can achieve scientific validation for a proposed small-sample classifier and its error estimate.     

DeepWILD: Wildlife Identification,Localisation and estimation on camera trap videos using Deep learning

Videos and images from camera traps are more and more used by ecologists to estimate the population of species on a territory. It is a laborious work since experts have to analyse massive data sets manually. This takes also a lot of time to filter these videos when many of them do not contain animals or are with human presence. Fortunately, deep learning algorithms for object detection can help ecologists to identify multiple relevant species on their data and to estimate their population. In this study, we propose to go even further by using object detection model to detect, classify and count species on camera traps videos. To this end, we developed a 3-step process: (i) At the first stage, after splitting videos into images, we annotate images by associating bounding boxes to each label thanks to MegaDetector algorithm; (ii) then, we extend MegaDetector based on Faster R-CNN architecture with backbone Inception-ResNet-v2 in order to not only detect the 13 relevant classes but also to classify them; (iii) finally, we design a method to count individuals based on the maximum number of bounding boxes detected. This final stage of counting is evaluated in two different contexts: first including only detection results (i.e. comparing our predictions against the right number of individuals, no matter their true class), then an evolved version including both detection and classification results (i.e. comparing our predictions against the right number in the right class). The results obtained during the evaluation of our model on the test data set are: (i) 73,92% mAP for classification, (ii) 96,88% mAP for detection with a ratio Intersection-Over-Union (IoU) of 0.5 (overlapping ratio between groundtruth bounding box and the detected one), and (iii) 89,24% mAP for detection at IoU = 0.75. Highly represented classes, like humans, have highest values of mAP around 81% whereas less represented classes in the train data set, such as dogs, have lowest values of mAP around 66%. Regarding the proposed counting method, we predicted a count either exact or $\pm$ 1 unit for 87% with detection results and for 48% with detection and classification results of our test data set. Our model is also able to detect empty videos. To the best of our knowledge, this is the first study in France about the use of object detection model on a French national park to locate, identify and estimate the population of species from camera trap videos.     

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.     

Comparisons and validation of statistical clustering techniques for microarray gene expression data

MOTIVATION: With the advent of microarray chip technology, large data sets are emerging containing the simultaneous expression levels of thousands of genes at various time points during a biological process. Biologists are attempting to group genes based on the temporal pattern of their expression levels. While the use of hierarchical clustering (UPGMA) with correlation 'distance' has been the most common in the microarray studies, there are many more choices of clustering algorithms in pattern recognition and statistics literature. At the moment there do not seem to be any clear-cut guidelines regarding the choice of a clustering algorithm to be used for grouping genes based on their expression profiles. RESULTS: In this paper, we consider six clustering algorithms (of various flavors!) and evaluate their performances on a well-known publicly available microarray data set on sporulation of budding yeast and on two simulated data sets. Among other things, we formulate three reasonable validation strategies that can be used with any clustering algorithm when temporal observations or replications are present. We evaluate each of these six clustering methods with these validation measures. While the 'best' method is dependent on the exact validation strategy and the number of clusters to be used, overall Diana appears to be a solid performer. Interestingly, the performance of correlation-based hierarchical clustering and model-based clustering (another method that has been advocated by a number of researchers) appear to be on opposite extremes, depending on what validation measure one employs. Next it is shown that the group means produced by Diana are the closest and those produced by UPGMA are the farthest from a model profile based on a set of hand-picked genes. Availability: S+ codes for the partial least squares based clustering are available from the authors upon request. All other clustering methods considered have S+ implementation in the library MASS. S+ codes for calculating the validation measures are available from the authors upon request. The sporulation data set is publicly available at http://cmgm.stanford.edu/pbrown/sporulation     

A pipeline for effectively developing highly polymorphic simple sequence repeats markers based on multi‐sample genomic data

Simple sequence repeats (SSRs) are widely used genetic markers in ecology, evolution, and conservation even in the genomics era, while a general limitation to their application is the difficulty of developing polymorphic SSR markers. Next‐generation sequencing (NGS) offers the opportunity for the rapid development of SSRs; however, previous studies developing SSRs using genomic data from only one individual need redundant experiments to test the polymorphisms of SSRs. In this study, we designed a pipeline for the rapid development of polymorphic SSR markers from multi‐sample genomic data. We used bioinformatic software to genotype multiple individuals using resequencing data, detected highly polymorphic SSRs prior to experimental validation, significantly improved the efficiency and reduced the experimental effort. The pipeline was successfully applied to a globally threatened species, the brown eared‐pheasant (Crossoptilon mantchuricum), which showed very low genomic diversity. The 20 newly developed SSR markers were highly polymorphic, the average number of alleles was much higher than the genomic average. We also evaluated the effect of the number of individuals and sequencing depth on the SSR mining results, and we found that 10 individuals and ~10X sequencing data were enough to obtain a sufficient number of polymorphic SSRs, even for species with low genetic diversity. Furthermore, the genome assembly of NGS data from the optimal number of individuals and sequencing depth can be used as an alternative reference genome if a high‐quality genome is not available. Our pipeline provided a paradigm for the application of NGS technology to mining and developing molecular markers for ecological and evolutionary studies.     

Statistical analysis of occupancy rates for overdispersed populations by redistribution procedures: Application to the european corn borer EGG masses distribution

Standard statistical analyses of distributions of individuals from contingency tables are generally invalid if the individuals are not distributed independently of each other. In this paper, we discuss a method of testing hypotheses about classification category occupancy rates for overdispersed population or for population whose individuals are distributed by groups rather than lonely. These methods are based on population redistribution simulations and provide valid, exact and powerful tests in situations for which classical methods are not appropriate. Illustrations are given from the European Corn Borer eggs data.     

A comparison of DNA‐based methods for delimiting species in a Cretan land snail radiation reveals shortcomings of exclusively molecular taxonomy

We compared the results of different approaches for delimiting species based on single‐locus DNA sequences with those of methods using binary multilocus data. As case study, we examined the radiation of the land snail genus Xerocrassa on Crete. Many of the methods based on mitochondrial sequences resulted in heavy under‐ or overestimations of the species number. The methods using AFLP data produced classifications with an on average higher concordance with the morphological classification than the methods based on mitochondrial sequences. However, the percentage of correct species classifications is low even with binary multilocus data. Gaussian clustering produced the classifications with the highest concordance with the morphological classification of all approaches applied in this study, both with single‐locus sequences and with binary multilocus data. There are two general problems that hamper species delimitation, namely rarity and the hierarchical structure of biodiversity. Methods for species delimitation using genetic data search for clusters of individuals, but do not implement criteria that are sufficient to distinguish clusters representing species from other clusters. The success of morphological species delimitation results from the potential to focus on characters that are directly involved in the speciation process, whereas molecular studies usually rely on markers that are not directly involved in speciation. © The Willi Hennig Society 2011.     

基于SVM和平均影响值的人肿瘤信息基因提取

基于基因表达谱的肿瘤分类信息基因选取是发现肿瘤特异表达基因、探索肿瘤基因表达模式的重要手段。借助由基因表达谱获得的分类信息进行肿瘤诊断是当今生物信息学领域中的一个重要研究方向,有望成为临床医学上一种快速而有效的肿瘤分子诊断方法。鉴于肿瘤基因表达谱样本数据维数高、样本量小以及噪音大等特点,提出一种结合支持向量机应用平均影响值来寻找肿瘤信息基因的算法,其优点是能够搜索到基因数量尽可能少而分类能力尽可能强的多个信息基因子集。采用二分类肿瘤数据集验证算法的可行性和有效性,对于结肠癌样本集,只需3个基因就能获得100%的留一法交叉验证识别准确率。为避免样本集的不同划分对分类性能的影响,进一步采用全折交叉验证方法来评估各信息基因子集的分类性能,优选出更可靠的信息基因子集。与基它肿瘤分类方法相比,实验结果在信息基因数量以及分类性能方面具有明显的优势。     

Evaluation of Gene Expression Classification Studies: Factors Associated with Classification Performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.     

Systematics of the genus Capra inferred from mitochondrial DNA sequence data

Traditional classification in the genus Capra is based mainly on horn morphology. However, previous investigations based on allozyme data are not consistent with this classification. We thus reexamined the evolutionary history of the genus by analyzing mitochondrial DNA (mtDNA) sequence variation. We collected bone samples from museums or dead animals found in the field. Thirty-four individuals were successfully sequenced for a portion of the mtDNA cytochrome b gene and control region (500 bp in total). We obtained a star-like phylogeny supporting a rapid radiation of the genus. In accordance with traditional classification, mtDNA data support the presence of two clades in the Caucasus and the hypothesis of a domestication event in the Fertile Crescent. However, in conflict with morphology, we found that C. aegagrus and C. ibex are polyphyletic species, and we propose a new scenario for Capra immigration into Europe.     

Homogeneity analysis: Assessing the utility of classifications and maps of natural resources

Abstract A quantitative measure of homogeneity, based on the average within-group association between samples, is proposed as a multivariate measure of the information content of classifications and maps. Homogeneity analyses are used to investigate questions of scale and the choice of attributes in the context of vegetation mapping. Previous studies in classification and mapping of soil types suggested that an optimum map scale or number of classification groups can be defined using homogeneity. This does not seem to be the case with vegetation data although homogeneity analysis can be used to define the coarsest acceptable scale and to quantity the benefits of mapping at finer scales. Homogeneity analysis is used here to compare the information content of classifications derived from various attributes with one based on the whole flora. For the data set examined, a classification derived from canopy species composition is as informative as one based on full rloristic composition for the scales at which we would normally map, whereas an environmental classification is less so. Further applications of homogeneity analysis are suggested.